Increasing spectral efficiency in a heterogeneous network

ABSTRACT

A technology for a user equipment (UE) in a heterogeneous network (HetNet). A modulation order can be selected for transmission from a small cell in the HetNet. A change in a UE state of the RRC idle mode can be identified. A desired coding rate can be identified to apply to the modulation order for a selected modulation and coding scheme (MCS) index. A predetermined transport block size (TBS) can be selected. Data in the TBS can be transmitted from the small cell to a UE using the MCS.

RELATED APPLICATIONS

This application claims the benefit of and hereby incorporates by reference U.S. Provisional Patent Application Ser. No. 61/821,635, filed May 9, 2013, with an attorney docket number P56618Z.

BACKGROUND

Users of wireless and mobile networking technologies are increasingly using their mobile devices to send and receive data as well as communicate. With increased data communications on wireless networks the strain on the limited bandwidth and system resources that are available for wireless telecommunications is also increasing. To handle the increasing amount of wireless services to an increasing numbers of users, an efficient use of the available radio network resources has become important.

In homogeneous networks, the transmission station, also referred to as a macro node, can provide basic wireless coverage to mobile devices within a defined geographic region, typically referred to as a cell. Heterogeneous networks (HetNets) were introduced to handle the increased traffic loads on the macro nodes due to increased usage and functionality of mobile devices. HetNets can include a layer of planned high power macro nodes (or macro-enhanced Node Bs) overlaid with layers of lower power nodes (micro-nodes, pico-nodes, femto-nodes, home-eNBs, relay stations, etc.) that can be deployed in a less well planned or even entirely uncoordinated manner within the coverage area of the macro nodes. The macro nodes can be used for basic coverage, and the low power nodes can be used to fill coverage holes, to improve capacity in hot-zones or at the boundaries between the macro nodes' coverage areas, and improve indoor coverage where building structures impede signal transmission.

Spectrum efficiency is the optimized use of spectrum or bandwidth so that the maximum amount of data can be transmitted with the fewest transmission errors. In a cellular network, spectrum efficiency measures how efficiently a limited frequency spectrum is utilized, such as the maximum number of users per cell that can be provided while maintaining an acceptable quality of service (QoS). For small nodes in a HetNet, enabling higher modulation orders in the small-node environments can enable greater spectral efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the disclosure will be apparent from the detailed description which follows, taken in conjunction with the accompanying drawings, which together illustrate, by way of example, features of the disclosure; and, wherein:

FIG. 1 depicts a multiple RAT (multi-RAT) HetNet with a macro-cell and a macro-node overlaided with layers of small nodes in accordance with an example;

FIG. 2 illustrates a method for transmitting data to a user equipment (UE) in a HetNet in accordance with an example;

FIG. 3 illustrates a method for transmitting data in a HetNet in accordance with an example;

FIG. 4 depicts the functionality of computer circuitry of a base station (BS) operable to communicate data in a HetNet in accordance with an example;

FIG. 5 depicts the functionality of computer circuitry of an enhanced node B (eNB) operable to select a modulation and coding scheme (MCS) for communication with a UE in accordance with an example;

FIG. 6 shows a table of various MCSs for a 256 QAM modulation order in accordance with an example;

FIG. 7 depicts the spectral efficiencies of a base station using the MCSs in accordance with an example;

FIG. 8 depicts the coding rates of a base station using different MCSs in accordance with an example;

FIG. 9 illustrates performance curves using a link level simulation (LLS) for a 64 quadrature amplitude modulation (QAM) and a 256 QAM with zero and non-zero values for the transmission Error Vector Magnitude (TX EVM) and reception EVM (RX EVM) in accordance with an example;

FIG. 10 depicts a table with the absolute values of the throughput values for a 256 QAM and its potential gains for selected EVM values compared to a 64 QAM with the same values of TX EVM and RX EVM in accordance with an example;

FIG. 11 illustrates system level simulation (SLS) results for a cluster of small nodes in accordance with an example;

FIG. 12 depicts a post-processing signal to interference plus noise ratio (SINR) distribution of small-cell UEs with different lambdas (λ) at 4% TX EVM in accordance with an example;

FIG. 13 illustrates average UE throughputs as a function of a full buffer traffic (FTP) model-1 arrival rate in accordance with an example;

FIG. 14 depicts a table with the throughput values achieved by small-cell UEs with 256 QAM modulations at different distribution points and average (AVG) in accordance with an example;

FIG. 15 shows a table with the average throughput values and 256 QAM gains for both macro-cell UEs and small-cell UEs in accordance with an example;

FIG. 16 illustrates 256 QAM throughput gains for both TX EVMs and RX EVMs in accordance with an example;

FIG. 17 depicts a table of the average throughput gains of small-cell UEs in accordance with an example; FIG. 18 depicts a table of the average throughput gains of UEs in accordance with an example;

FIG. 19 depicts SLS evaluation parameters in accordance with an example;

FIG. 20 illustrates the block error rate (BLER) vs. the signal to noise ratio (SNR) for different MCSs in accordance with an example;

FIG. 21 depicts the log likelihood ratio (LLR) distribution for different SNR values in accordance with an example;

FIG. 22 depicts another the LLR distribution for different SNR values in accordance with an example;

FIG. 23 depicts the mean mutual information per bit (MMIB) versus the SNR in accordance with an example;

FIG. 24 depicts the BLER vs. MMIB for different MCSs in accordance with an example;

FIG. 25 depicts a table with different MCSs for selected parameters in accordance with an example;

FIG. 26 illustrates a graph of curve fitted line of MMIB vs. SNR in accordance with an example; and

FIG. 27 illustrates a diagram of a UE in accordance with an example.

Reference will now be made to the exemplary embodiments illustrated, and specific language will be used herein to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended.

DETAILED DESCRIPTION

Before the present invention is disclosed and described, it is to be understood that this invention is not limited to the particular structures, process steps, or materials disclosed herein, but is extended to equivalents thereof as would be recognized by those ordinarily skilled in the relevant arts. It should also be understood that terminology employed herein is used for the purpose of describing particular examples only and is not intended to be limiting. The same reference numerals in different drawings represent the same element. Numbers provided in flow charts and processes are provided for clarity in illustrating steps and operations and do not necessarily indicate a particular order or sequence.

Mobile devices are increasingly equipped with multiple radio access technologies (RATs). The mobile devices can be configured to connect to and choose among different access networks provided by the RATs. In homogeneous networks, the base stations or macro nodes can provide basic wireless coverage to mobile devices in an area covered by the node. HetNets can include a layer of macro nodes or macro-eNBs overlaid with layers of small cells, also referred to as low power cells, low power nodes or small nodes, such as micro-nodes, pico-nodes, femto-nodes, home-eNBs, relay stations, etc. In addition, other RATs, such as Institute of Electronics and Electrical Engineers (IEEE) 802.11 configured access points (APs) can be interspersed with the low power nodes.

The macro nodes can be used for basic coverage and the small nodes or low power nodes can be used to fill coverage holes, to improve capacity in hot-zones or at the boundaries between the macro nodes' coverage areas, and improve indoor coverage where building structures impede signal transmission. Small nodes, such as femto nodes, can also be indoor nodes in houses, apartments, offices, or other indoor locations. In one embodiment, small nodes can support closed subscriber group (CSG) functionality. Small nodes, such as pico cells, can be used for coverage in halls and airports. Small nodes can also be used as outdoor nodes. Small nodes can also be used at lower heights compared to macro nodes. FIG. 1 depicts a multi-RAT HetNet with a macro-cell 110 and a macro-node 120 overlaided with layers of lower power or small nodes including micro-nodes 130, pico-nodes 140, femto-nodes 150, and WiFi access points (APs) 160.

High demand for increased throughput by UEs can be satisfied by deploying a cluster of small nodes to provide an acceptable quality of service (QoS) for the UEs. In one embodiment, dense clusterization of small nodes can be used at hotspots to provide closer serving nodes to more UEs for better network capacity. However, as the number of small nodes deployed in a given area increases, the inter-small node interference also increase. As inter-small node interference reaches a threshold limit, there is an upper bound constraint on the number of small nodes to be deployed in a hotspot area. Traditionally, the disadvantage of the high density deployment or clusterization of small nodes is the level of inter-small node interference, e.g. the level of interference that occurs between multiple small nodes in a dense area. The inter-small node interference decreases the signal to noise ratio (SNR) and/or the signal to interference plus noise ratio (SINR) between UEs and small nodes, resulting in lower or decreased UE throughput. To maintain an optimum density of small node deployments, inter-small node interference is minimized while UE throughput is maximized.

In one embodiment, small areas provide higher SNR which allows the use of higher modulation and coding schemes (MCS), such as 64 quadrature amplitude modulation (QAM), 128 QAM, or 256 QAM. As the distance or separation between small nodes and UEs decrease, propagation losses and interference levels decrease and better channel conditions and SNR or SINR are maintained between the UEs and the small nodes. In one embodiment, interference mitigation techniques can be used to improve or boost the SNR and/or SINR of a UE so that the UEs can use higher modulation schemes with an acceptable error rate. In one embodiment, higher performance for small nodes, such as high throughput and low interference, can be achieved by using higher order modulation schemes, such as 256 QAM. When higher order modulations are used, signal imperfections, such as signal interference, at the UE and small node are reduced because the distance between constellation points are shorter.

In one embodiment, the optimum number of small nodes per hotspot is determined to achieve the highest user average throughput with minimal inter-small node interference. The optimum number of small nodes can be determined by evaluating the feasibility of high modulation order schemes, such as 256 QAM. In one embodiment, the small nodes can be outdoor small nodes. In another embodiment, UEs can operate in a high SINR region to use a 256 QAM. In one embodiment, multiple UEs can be clustered in an indoor hotspot environment and are served by small nodes. In another embodiment, when a small node varies channels the macro node can be configured to vary channels as well. In one embodiment, inter-frequency variation can be used for the macro nodes and small nodes, where different frequencies are used for the macro nodes and the small nodes.

FIG. 2 illustrates a method 200 for transmitting data to a UE cell in a HetNet. The method can comprise the operation of selecting a modulation order for transmission from a small node in the HetNet, as in block 210. In one embodiment, selecting the modulation order further comprises selecting a modulation order of 8 for the MCS index. In one embodiment, the modulation order of 8 is selected when a UE has an effective SINR of greater than 20 decibels. In one embodiment, the method further comprises setting the effective SINR threshold within a range of 20 decibels to 26.5 decibels. In another embodiment, selecting the modulation order further comprises selecting a modulation of 256 QAM for the modulation order.

The method 200 can further comprise the operation of identifying a desired coding rate to apply to the modulation order for a selected MCS index, as in block 220. In one embodiment, identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate of less than or equal to 0.94. In another embodiment, identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate with a range from approximately 0.70 to 0.94. The method 200 can also comprise the operation of selecting a predetermined transport block size (TBS), as in block 230. In one embodiment, selecting the predetermined TBS further comprises selecting a TBS wherein a number of physical resource blocks (PRBs) is 5, 10, 25, 50, 100, or 110. The method may further comprise the operation of transmitting data in the TBS from the small node to a UE using the MCS, as in block 240.

In one embodiment, the method further comprises: assigning a MCS index of 29 for a modulation order of 8 and a coding rate of 0.7017; assigning a MCS index of 30 for a modulation order of 8 and a coding rate of 0.7283; assigning a MCS index of 31 for a modulation order of 8 and a coding rate of 0.755; assigning a MCS index of 32 for a modulation order of 8 and a coding rate of 0.7817; assigning a MCS index of 33 for a modulation order of 8 and a coding rate of 0.8083; assigning a MCS index of 34 for a modulation order of 8 and a coding rate of 0.8217; assigning a MCS index of 35 for a modulation order of 8 and a coding rate of 0.8483; assigning a MCS index of 36 for a modulation order of 8 and a coding rate of 0.875; assigning a MCS index of 37 for a modulation order of 8 and a coding rate of 0.915; and/or assigning a MCS index of 38 for a modulation order of 8 and a coding rate of 0.9417.

FIG. 3 illustrates a method 300 for transmitting data in a HetNet. The method can comprise the operation of selecting a modulation order of 8 (2⁸) for an MCS index to transmit the data in the HetNet, as in block 310. In one embodiment, selecting the modulation order further comprises selecting a modulation of 256 QAM for the modulation order. The method 300 can be further comprise the operation of identifying a coding rate for the modulation order to enable the data to be received with a desired block error rate (BLER), as in block 320. In one embodiment, identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate of less than or equal to 0.94. In another embodiment, identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate with a range from approximately 0.70 to 0.94. The method 300 can also comprise the operation of identifying a selected transport block size (TBS), as in block 330. The method 300 can further comprise the operation of transmitting the data from a cell in the HetNet using the MCS, as in block 340. In one embodiment, transmitting the data further comprises transmitting the data from a small cell in the HetNet using the MCS.

Another example provides functionality 400 of computer circuitry of a base station operable to communicate data in a HetNet, as shown in the flow chart in FIG. 4. The functionality can be implemented as a method or the functionality can be executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine readable storage medium. The computer circuitry can be configured to measure an SINR for communication with a UE in the HetNet, as in block 410. The computer circuitry can be further configured to select a modulation order of eight when the SINR is greater than a selected threshold, as in block 420. In one embodiment, the computer circuitry is further configured to set the threshold to at least 20 decibels for the SINR. In another embodiment, the computer circuitry is further configured to set the threshold within a range of 20 decibels to 26.5 decibels. For a fixed modulation order, the threshold level can change as the coding rate changes. For example if a high SINR is available for the fixed modulation order then a higher coding rate can be selected to enable a higher throughput. The computer circuitry can also be configured to determine a coding rate for the modulation order for a selected MCS index value, as in block 430. The computer circuitry can also be configured to determine a selected TBS, as in block 440. The computer circuitry can also be configured to communicate data to the UE using the MCS and the TBS, as in block 450.

In one embodiment, the computer circuitry is further configured to: assign a MCS index of 29 for a modulation order of 8 and a coding rate of 0.7017; assign a MCS index of 30 for a modulation order of 8 and a coding rate of 0.7283; assign a MCS index of 31 for a modulation order of 8 and a coding rate of 0.755; assign a MCS index of 32 for a modulation order of 8 and a coding rate of 0.7817; assign a MCS index of 33 for a modulation order of 8 and a coding rate of 0.8083; assign a MCS index of 34 for a modulation order of 8 and a coding rate of 0.8217; assign a MCS index of 35 for a modulation order of 8 and a coding rate of 0.8483; assign a MCS index of 36 for a modulation order of 8 and a coding rate of 0.875; assign a MCS index of 37 for a modulation order of 8 and a coding rate of 0.915; or assign a MCS index of 38 for a modulation order of 8 and a coding rate of 0.9417.

Another example provides functionality 500 of computer circuitry of a low power cell operable to select an MCS for communication with a UE, as shown in the flow chart in FIG. 5. The functionality can be implemented as a method or the functionality can be executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine readable storage medium. In one embodiment, the lower power cell can include micro-nodes, pico-nodes, femto-nodes, home-eNBs, relay stations, and/or WiFi access points. The computer circuitry can be configured to select a modulation order with a 256 QAM for transmission from a cell in the HetNet, as in block 510. In one embodiment, the computer circuitry is further configured to select a modulation order of 8 for the MCS index. In one embodiment, the computer circuitry is further configured to select the modulation order of 8 when a UE has an effective SINR of greater than 20 decibels. In another embodiment, the computer circuitry is further configured to select a modulation of 256 QAM for the modulation order. The computer circuitry can be further configured to identify an information rate to apply to the modulation order for a selected MCS index, as in block 520. The computer circuitry can also be configured to select a defined TBS, wherein the predetermined TBS is selected to achieve a higher throughput using the 256 QAM, as in block 530. In one embodiment, the computer circuitry is further configured to select a TBS wherein a number of physical resource blocks (PRBs) is 5, 10, 25, 50, 100, or 110. The computer circuitry can also be configured to transmit data in the TBS from the cell to a UE using the MCS, as in block 540. In one embodiment, the computer circuitry is further configured to select a channel coding to provide the coding rate of less than or equal to 0.94. In another embodiment, the computer circuitry is further configured to select a channel coding to provide the coding rate with a range from approximately 0.70 to 0.94.

FIG. 6 shows a table of various MCSs for a 256 QAM modulation order. The table in FIG. 6 further depicts coding rates that range between 0.7-0.95 and the corresponding TBSs. FIG. 6 also shows the TBSs for a few selected numbers of PRBs along with their coding rates and achievable spectral efficiencies. In one embodiment as shown in FIG. 6, the 256 QAM MCS indices start with index 29. In one embodiment, additional TBSs, such as 79728, 82548, 85356, 86772, 89580, 92400, 96624, 99444, are needed to achieve higher throughput using a 256 QAM modulation order. FIG. 6 is not intended to be limiting. Other coding rates and higher modulation orders may also be used.

FIG. 7 depicts the spectral efficiencies of a downlink of a base station for the coding rates in FIG. 6 for modulation orders of 2, 4, 6 and 8. FIG. 7 also illustrates that that 256 QAM may be considered on the transmission layer when the effective SINR is above 20 dB. FIG. 7 also illustrates that the first MCS of 256 QAM (i.e. the circle depicting the lowest coding rate for a modulation order of 8) and the last MCS of 64 QAM (i.e. the circle depicting the highest coding rate for a modulation order of 6) produce approximately the same spectral efficiency. FIG. 8 depicts the coding rates for a downlink of a base station relative to SNR for the coding rates listed in FIG. 6 for modulation orders of 2, 4, 6 and 8.

FIG. 9 illustrates performance curves using a link level simulation (LLS) for 64 QAM and 256 QAM with zero and non-zero values for the TX error vector magnitude (EVM) and RX EVM. FIG. 9 further illustrates that at a high SNR and zero TX EVM or RX EVM values, the 256 QAM throughput is higher than the 64 QAM throughput, with a maximum gain of 23.1 percent for an SNR of 40 dB. FIG. 9 also illustrates that for a 4 percent TX EVM, the average throughput values of the 64 QAM and the 256 QAM are lower than the zero-EVM case. FIG. 9 also illustrates the 256 QAM gain compared to the 64 QAM is reduced to 9.4% for an SNR of 40 dB. FIG. 9 also illustrates that for a TX EVM at 4 percent and a RX EVM at 4 percent, the average throughput values of the two modulation orders are degraded and are substantially the same.

The table in FIG. 10 shows the absolute values of the throughput values for a 256 QAM and its potential gains for selected EVM values compared to a 64 QAM with the same values of TX EVM and RX EVM. The table in FIG. 10 further illustrates that most of the original gain under ideal condition will diminish when both the TX EVM and the RX EVM are considered.

FIG. 11 illustrates a cluster of a plurality of small nodes for a system level simulation (SLS). In one embodiment, for different impairments the throughput gains will vary. In one example, an inter-frequency mode is used in which a small node operates at 3.5 GHz and an reference signal received quality (RSRQ) based UE association rule is used, where a full load and zero cell-range expansion (CRE) are used. Additionally in one the example, SLS evaluation parameters are used for the SLS, such as the parameters shown in FIG. 20. In one embodiment, when an RSRQ association rule is used for the network as in FIG. 11, 50.36% of the UEs are associated with the macro cell and 49.64% of the UEs are associated with the four small nodes. In one embodiment, a 256 QAM with throughput gains with TX EVM is used.

FIG. 12 depicts a post-processing SINR distribution of small-cell UEs with different lambdas (λ) at 4% TX EVM. In one embodiment, a 4% TX EVM is used for small nodes and an 8% TX EVM is used for macro cells. In another embodiment, a non-full buffer traffic model (FTP model-1) is used with arrival rates of λ, where the different values of λ correspond to a range of cell resource utilization (RU).

FIG. 12 also depicts the CDF of the average post-processing SINR of the small-cell UEs for different λ, e.g. traffic rates. FIG. 12 illustrates that as SINR charging data function (CDF) decreases λ increases because the higher loading rate the more transmission and hence more interference there is. For example, when λ is 4, 68% of the PDSCH transmission post SINR has an SINR above 20 decibels. FIG. 12 also illustrates that the post-processing SINR is upper bounded by 28 dB because of the 4% TX EVM.

FIG. 13 illustrates the average UE throughputs as a function of a file transfer protocol (FTP) model-1 arrival rate. FIG. 13 depicts the average UE throughput for the UEs using small nodes, the UEs using macro nodes, and all the UEs. FIG. 13 also depicts the potential 256 QAM gains, where small nodes employ both a 64 QAM and a 256 QAM. FIG. 13 also illustrates that the average UE throughput decreases with 2. In one example, the average UE throughput decrease because as λ increases the cells start to transmit more data causing more interference to the neighboring UEs, e.g. lowering the SINR per UE. In another example, the average UE throughput decreases because the cells get congested serving higher traffic as λ increases, e.g. longer periods to serve each UE. FIG. 13 depicts that for maximum modulation orders in small nodes, a 256 QAM achieves higher UE throughput for selected λ values. For example, when is 12 (e.g. the small node RU is 23.89%), the throughput of a UE using a small node increases by 10.1% due to employing 256 QAM modulation order. In another example, for all the UEs, both UEs using macro nodes and UEs using small nodes, where λ is 12, the average all UEs gain is 10.03%, and the average RU of all the cells is 37.32%.

The table in FIG. 14 shows the throughput values achieved by the UEs using small nodes with 256 QAM modulations at different distribution points, e.g. 5%, 50%, 95%, and average (AVG). FIG. 14 also shows the percentage gains of a 256 QAM compared to a 64 QAM. The AVG throughput gains of the small-cell UEs ranges from 10.1% to 12.85%. FIG. 14 also shows the resource utilization (RU) for each small node, respectively.

The table in FIG. 15 shows the average throughput values and 256 QAM gains for UEs using a macro node and for UEs using small nodes. FIG. 15 shows that the average throughput gains of all the UEs ranges from 7.91% to 10.27%. FIG. 15 also shows that the average RU percentages per macro node and for small nodes, such as in FIG. 11.

FIG. 16 illustrates 256 QAM throughput gains for both TX EVM and RX EVM. FIG. 16 also shows the potential gains of a 256 QAM with receiver impairments. In one embodiment, the receiver impairments can include RX local oscillator phase noise, RX dynamic range, in-phase/quadrature (I/Q) imbalance, carrier leakage, and carrier frequency offset. In FIG. 16, RX EVM is set to 4% for a starting value to show the potential gains of the 256 QAM. FIG. 16 also depicts the average UE throughputs values, similar to FIG. 13. In contrast to the average throughput values when using the TX EVM in FIG. 13, the throughput values are smaller for when using both TX EVM and RX EVM as in FIG. 16. The throughput values in FIG. 16 may be smaller because of the impact of the RX EVM.

The table in FIG. 17 shows the gains and RU for UEs using small nodes with TX EVM and RX EVM. The table in FIG. 18 shows the gains and RU for the UEs using small nodes and macro nodes for the TX EVM and RX EVM cases. Compared to the tables in FIGS. 14 and 15, the absolute values of the UE throughput gains are lower in the tables in FIGS. 17 and 18. In one embodiment, because of the 4% RX EVM, the UE throughput gains of the 256 QAM are lower in FIGS. 17 and 18 than in the tables in FIGS. 14 and 15. The table in FIG. 17 also shows that the average throughput gains of the UEs using the small nodes (small-cell UEs) range from 5.97% to 8.59%. The table in FIG. 18 shows that the average throughput gains of all the UEs range from 4.38% to 7.52%.

The table in FIG. 19 shows simulation parameters, such as those used in FIG. 11 and FIGS. 20-25.

FIG. 20 illustrates the BLER vs. SINR for different coding rates. In one embodiment, using the BLER vs. SNR for different MCSs shown in FIG. 21, the log likelihood ratio (LLR) per bit for all SNR values is calculated using the following equation based on the log distance between the constellation points and the received symbols:

${{{LLR}_{I}\left( b_{k} \right)} = {{\ln {\sum\limits_{A \in {({{s:b_{k}} = 1})}}{\exp\left( {- \frac{\left( {z - A} \right)^{2}}{2\sigma^{2}}} \right)}}} - {\ln {\sum\limits_{B \in {({{s:b_{k}} = {- 1}})}}{{\exp\left( {- \frac{\left( {z - B} \right)^{2}}{2\sigma^{2}}} \right)}.}}}}},$

where a is distance between constellation points, s is a symbol, B_(k) is a value between 0 and 7 (e.g. 8 bits for a 256 QAM), z is the receive symbol after adding the noise, A is the concatenation point, z-A is the receive minus the position of the constellation points and z-A is on quadrature axis, z-B the receive minus the position of the constellation points and z-B is on an axis different than z-A, and where 6 is a noise variance at a selected SNR.

FIGS. 21 and 22 depict the LLR distribution of bit 1 and bit 2 for different SNR values. In one embodiment, using the LLR distribution at selected SNR values as shown in FIGS. 21 and 22, the mean mutual information in bit (MMIB) is calculated using:

${{I\left( {{LLR}_{d};d} \right)} = {{\frac{1}{2}{\int_{- \infty}^{+ \infty}{{P_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 1} \right)}\log_{2}\frac{p_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 1} \right)}{p_{{LLR}_{d}}(l)}\ {l}}}} + {\frac{1}{2}{\int_{- \infty}^{+ \infty}{{p_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 0} \right)}\log_{2}\ \frac{p_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 0} \right)}{p_{{LLR}_{d}}(l)}{l}}}}}},\mspace{79mu} {{{where}\mspace{14mu} {p_{{LLR}_{d}}(l)}} = {\left\lbrack {{p_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 1} \right)} + {p_{{LLR}_{d}}\left( {\left. l \middle| d \right. = 0} \right)}} \right\rbrack/2}}$   and ${{MMIB}_{256{QAM}} = {\frac{I\left( {{LLR}_{d\; 0};{d\; 0}} \right)}{4} + \frac{I\left( {{LLR}_{d\; 1};{d\; 1}} \right)}{4} + \frac{I\left( {{LLR}_{d\; 2};{d\; 2}} \right)}{4} + \frac{I\left( {{LLR}_{d\; 3};{d\; 3}} \right)}{4}}},$

where d is the transmitted bits, Z is equal to 1 for the first part and Z is equal to 0 for the second part, P_(LLR) is the probability.

FIG. 23 depicts the MMIB versus the SNR using the equations above.

FIG. 24 depicts the BLER vs. MMIB for different coding rates. In one embodiment, the BLER can be approximated using:

${{{CBLER}_{i}(X)} = {\frac{1}{2}\left\lbrack {1 - {{erf}\left( \frac{x - b_{S,M}}{\sqrt{2}c_{S,M}} \right)}} \right\rbrack}},$

where x is the MMIB and b and c are embedded in the PHY abstraction model.

The table in FIG. 25 illustrates different MCSs for selected b and c parameters as in the equation above. In one embodiment, for a selected SINR, the BLER and MCS are obtained by approximating the MMIB-SNR expression to obtain the graph of the MMIB in FIG. 26, approximating the BLER-MMIB Gaussian expression to get BLER using b and c from the table in FIG. 25, and selecting the highest MCS to achieve the necessary or desired BLER.

FIG. 26 illustrates the MMIB vs. SNR for the following curve fitting equations:

${J(x)} \approx \left\{ \begin{matrix} {{{a_{1}x^{3}} + {b_{1}x^{2}} + {c_{1}x}},} & {{{if}\mspace{14mu} x} \leq 1.6363} \\ {1 - {\exp \left( {{a_{2}x^{3}} + {b_{x}x^{2}} + {c_{2}x} + d_{2}} \right)}} & {{{{if}\mspace{14mu} 1.6363} \leq x \leq \infty},} \end{matrix} \right.$

where a1, a2, b1, b2, c1, c2, d2 are constants. The approximate formula is given by:

MMIB₈=0.1495J(1.6662√{square root over (SNR)})+0.0435J(9.1275√{square root over (SNR)})+0.2803J(0.7018√{square root over (SNR)})+0.5257J(0.2177√{square root over (SNR)})

FIG. 27 provides an example illustration of the wireless device, such as a user equipment (UE), a mobile station (MS), a mobile wireless device, a mobile communication device, a tablet, a handset, or other type of wireless device. The wireless device can include one or more antennas configured to communicate with a node or transmission station, such as a base station (BS), an evolved Node B (eNB), a baseband unit (BBU), a remote radio head (RRH), a remote radio equipment (RRE), a relay station (RS), a radio equipment (RE), a remote radio unit (RRU), a central processing module (CPM), or other type of wireless wide area network (WWAN) access point. The wireless device can be configured to communicate using at least one wireless communication standard including 3GPP LTE, WiMAX, High Speed Packet Access (HSPA), Bluetooth, and Wi-Fi. The wireless device can communicate using separate antennas for each wireless communication standard or shared antennas for multiple wireless communication standards. The wireless device can communicate in a wireless local area network (WLAN), a wireless personal area network (WPAN), and/or a WWAN.

FIG. 27 also provides an illustration of a microphone and one or more speakers that can be used for audio input and output from the wireless device. The display screen can be a liquid crystal display (LCD) screen, or other type of display screen such as an organic light emitting diode (OLED) display. The display screen can be configured as a touch screen. The touch screen can use capacitive, resistive, or another type of touch screen technology. An application processor and a graphics processor can be coupled to internal memory to provide processing and display capabilities. A non-volatile memory port can also be used to provide data input/output options to a user. The non-volatile memory port can also be used to expand the memory capabilities of the wireless device. A keyboard can be integrated with the wireless device or wirelessly connected to the wireless device to provide additional user input. A virtual keyboard can also be provided using the touch screen.

Various techniques, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, non-transitory computer readable storage medium, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the various techniques. In the case of program code execution on programmable computers, the computing device can include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. The volatile and non-volatile memory and/or storage elements can be a RAM, EPROM, flash drive, optical drive, magnetic hard drive, or other medium for storing electronic data. The base station and mobile station can also include a transceiver module, a counter module, a processing module, and/or a clock module or timer module. One or more programs that can implement or utilize the various techniques described herein can use an application programming interface (API), reusable controls, and the like. Such programs can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language, and combined with hardware implementations.

It should be understood that many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module can be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

Modules can also be implemented in software for execution by various types of processors. An identified module of executable code can, for instance, comprise one or more physical or logical blocks of computer instructions, which can, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but can comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module.

Indeed, a module of executable code can be a single instruction, or many instructions, and can even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data can be identified and illustrated herein within modules, and can be embodied in any suitable form and organized within any suitable type of data structure. The operational data can be collected as a single data set, or can be distributed over different locations including over different storage devices, and can exist, at least partially, merely as electronic signals on a system or network. The modules can be passive or active, including agents operable to perform desired functions.

Reference throughout this specification to “an example” means that a particular feature, structure, or characteristic described in connection with the example is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in an example” in various places throughout this specification are not necessarily all referring to the same embodiment.

As used herein, a plurality of items, structural elements, compositional elements, and/or materials can be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group without indications to the contrary. In addition, various embodiments and example of the present invention can be referred to herein along with alternatives for the various components thereof. It is understood that such embodiments, examples, and alternatives are not to be construed as defacto equivalents of one another, but are to be considered as separate and autonomous representations of the present invention.

Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided, such as examples of layouts, distances, network examples, etc., to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention can be practiced without one or more of the specific details, or with other methods, components, layouts, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention.

While the forgoing examples are illustrative of the principles of the present invention in one or more particular applications, it will be apparent to those of ordinary skill in the art that numerous modifications in form, usage and details of implementation can be made without the exercise of inventive faculty, and without departing from the principles and concepts of the invention. Accordingly, it is not intended that the invention be limited, except as by the claims set forth below. 

1. A method for transmitting data to a user equipment (UE) in a heterogeneous network (HetNet), comprising: selecting a modulation order for transmission from a small cell in the HetNet; identifying a desired coding rate to apply to the modulation order for a selected modulation and coding scheme (MCS) index; selecting a predetermined transport block size (TBS); and transmitting data in the TBS from the small cell to a UE using the MCS.
 2. The method of claim 1, wherein selecting the modulation order further comprises selecting a modulation order of 8 for the MCS index.
 3. The method of claim 1, wherein selecting the modulation order further comprises selecting a modulation order of 8 when an effective SINR with the UE is greater than 20 decibels.
 4. The method of claim 1, wherein selecting the modulation order further comprises selecting a modulation of 256 quadrature amplitude modulation (QAM) for the modulation order.
 5. The method of claim 1, wherein identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate of less than or equal to 0.94.
 6. The method of claim 1, wherein selecting the predetermined TB S further comprises selecting a TBS when a number of physical resource blocks (PRBs) is 5, 10, 25, 50, 100, or
 110. 7. The method of claim 1, wherein the method further comprises: assigning an MCS index of 29 for a modulation order of 8 and a coding rate of 0.7017; assigning an MCS index of 30 for a modulation order of 8 and a coding rate of 0.7283; assigning an MCS index of 31 for a modulation order of 8 and a coding rate of 0.755; assigning an MCS index of 32 for a modulation order of 8 and a coding rate of 0.7817; assigning an MCS index of 33 for a modulation order of 8 and a coding rate of 0.8083; assigning an MCS index of 34 for a modulation order of 8 and a coding rate of 0.8217; assigning an MCS index of 35 for a modulation order of 8 and a coding rate of 0.8483; assigning an MCS index of 36 for a modulation order of 8 and a coding rate of 0.875; assigning an MCS index of 37 for a modulation order of 8 and a coding rate of 0.915; or assigning an MCS index of 38 for a modulation order of 8 and a coding rate of 0.9417.
 8. A method for transmitting data in a heterogeneous network (HetNet), comprising: selecting a modulation order of 8 for a modulation and coding scheme (MCS) index to transmit the data in the HetNet; identifying a coding rate for the modulation order to enable the data to be received with a desired block error rate (BLER); identifying a selected transport block size (TBS); and transmitting the data from a cell in the HetNet using the MCS.
 9. The method of claim 8, wherein transmitting the data further comprises transmitting the data from a small cell in the HetNet using the MCS.
 10. The method of claim 8, wherein selecting the modulation order further comprises selecting a modulation of 256 quadrature amplitude modulation (QAM) for the modulation order.
 11. The method of claim 8, wherein identifying the desired coding rate further comprises selecting a forward error correction code to provide the coding rate of less than or equal to 0.94.
 12. A base station (BS) operable to communicate data in a heterogeneous network (HetNet), the BS having computer circuitry configured to: measure a signal to interference plus noise ratio (SINR) for communication with a user equipment (UE) in the HetNet; select a modulation order of 8 when the SINR is greater than a selected threshold; determine a coding rate for the modulation order for a selected modulation and coding scheme (MCS) index value; determine a selected transport block size (TB S); and communicate data to the UE using the MCS and the TBS.
 13. The computer circuitry of claim 12, wherein the computer circuitry is further configured to set the threshold to at least 20 decibels for the SINR.
 14. The computer circuitry of claim 12, wherein the computer circuitry is further configured to: assign an MCS index of 29 for a modulation order of 8 and a coding rate of 0.7017; assign an MCS index of 30 for a modulation order of 8 and a coding rate of 0.7283; assign an MCS index of 31 for a modulation order of 8 and a coding rate of 0.755; assign an MCS index of 32 for a modulation order of 8 and a coding rate of 0.7817; assign an MCS index of 33 for a modulation order of 8 and a coding rate of 0.8083; assign an MCS index of 34 for a modulation order of 8 and a coding rate of 0.8217; assign an MCS index of 35 for a modulation order of 8 and a coding rate of 0.8483; assign an MCS index of 36 for a modulation order of 8 and a coding rate of 0.875; assign an MCS index of 37 for a modulation order of 8 and a coding rate of 0.915; or assign an MCS index of 38 for a modulation order of 8 and a coding rate of 0.9417.
 15. A low power cell operable to select a modulation and coding scheme (MCS) for communication with a user equipment (UE), the low power cell having computer circuitry configured to: select a modulation order with a 256 quadrature amplitude modulation (QAM) for transmission from a cell in the HetNet; identify an information rate to apply to the modulation order for a selected modulation and coding scheme (MCS) index; select a defined transport block size (TBS), wherein the predetermined TBS is selected to achieve a higher throughput using the 256 QAM; and transmit data in the TBS from the cell to a user equipment (UE) using the MCS.
 16. The computer circuitry of claim 15, wherein the computer circuitry is further configured to select a modulation order of 8 for the MCS index.
 17. The computer circuitry of claim 16, wherein the computer circuitry is further configured to select the modulation order of 8 when an effective SINR with the UE is greater than 20 decibels.
 18. The computer circuitry of claim 15, wherein the computer circuitry is further configured to select a modulation of 256 quadrature amplitude modulation (QAM) for the modulation order.
 19. The computer circuitry of claim 15, wherein the computer circuitry is further configured to select a channel coding to provide the coding rate of less than or equal to 0.94.
 20. The computer circuitry of claim 15, wherein the computer circuitry is further configured to select a TBS when a number of physical resource blocks (PRBs) is 5, 10, 25, 50, 100, or
 110. 